• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2258-2263
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• 2003

This paper presents the design and development of an optimal fuzzy logic controller (FLC) for a laser tracking system. An optimal intelligent fuzzy logic controller was founded on integral criterion of the fuzzy models and three-dimensional fuzzy control. Research had been also concentrated on the methods for multivariable fuzzy models for the purposes of real-time process. Simulation results have shown remarkable tracking performance of this fuzzy PID controller.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.457-459
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• 2003

This paper considers the application of Fuzzy Logic to Smart Decision process of Smart Sensor system that interprets and response to the change of environmental parameters. The considered system consists of three sensors: temperature sensor, humidity sensor and pressure sensor. The smartness of system is constituted by the applying of Fuzzy Logic. The paper discusses the technical details of the application of Fuzzy Logic for making the system to be smarter.

• Journal of Information Management
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• v.24 no.3
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• pp.73-100
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• 1993

In attempting to respond to boolean retrieval system's limitations, this paper presents the design of a retrieval system using fuzzy logic. The fuzzy retrieval system introduces the weights of terms in the documents and in the query and makes use of them to determine how much relevant a document is to the given query. After comparing and analyzing the previous researches, an effective model of the fuzzy retrieval system is suggested and the performance of the system is evaluated through actual examples.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.240-245
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• 1998

Fuzzy information retrieval which uses the concept of fuzzy relation is able to retrieve documents in the way based on not morphology but semantics, dissimilar to traditional information retrieval theories. Fuzzy information retrieval logically consists of three sets : the set of documents, the set of terms and the set of queries. It maintains a fuzzy relational matrix which describes the relationship between documents and terms and creates a thesaurus with fuzzy relational product. It also provides the user with documents which are relevant to his query. However, there are some problems on building a thesaurus with fuzzy relational product such that it has big time complexity and it uses fuzzy values to be processed with flating-point. Actually, fuzzy values have to be expressed and processed with floating-point. However, floating-point operations have complex logics and make the system be slow. If it is possible to exchange fuzzy values with binary values, we could expect sp eding up building the thesaurus. In addition, binary value expressions require just a bit of memory space, but floating -point expression needs couple of bytes. In this study, we suggest a new method of building a thesaurus, which accelerates the operation of the system by pre-applying an ${\alpha}$-cut. The experiments show the improvement of performance and reliability of the system.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1267-1271
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• 2003

This paper proposes an indirect adaptive fuzzy controller for general SISO nonlinear systems. No a priori information on bounding constants of uncertainties including reconstruction errors and optimal fuzzy parameters is needed. The control law and the update laws for fuzzy rule structure and estimates of fuzzy parameters and bounding constants are determined so that the Lyapunov stability of the whole closed loop system is guaranteed. The computer simulation results for an inverted pendulum system show the performance of the proposed robust adaptive fuzzy controller.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.111-115
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• 2001

In this paper, a simple and systematic control design method is proposed for a discrete-time Takagi-Sugeno(TS) fuzzy system, which employs the parallel distributed compensation(PDC) to determine the structure of a fuzzy controller so as to mark all the Lyaunov exponents of the controlled TS fuzzy system strictly positive. This approach is proven to be mathematically rigorous for anticontrol of chaos for a TS fuzzy system in the sense that any given discrete-time TS fuzzy system can be made chaotic by the designed PDC controller along with the-operation. A numerical example is included to visualize the anticontrol effect.

• Journal of Ocean Engineering and Technology
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• v.11 no.1
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• pp.36-43
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• 1997

Weld bead shape is an important measure for evaluation of weld quality. Many welding parameters have influence on the weld bead shape. The quantitative relationship between welding parameters and bead shape, however, is not determined yet because of their high complexity and many unknown factors. Fuzzy expert system is an advanced expert system which uses fuzzy rules and approximate reasoning. It is a vert useful tool for welding technology because is can process rationally the uncertain and inexact information such as the welding information. In this paper, the empirical and the qualitative relationship between welding parameters and bead shape are analyzed and represented by fuzzy rules. They are converted to the quantitative relationship by use of approximate reasoning of fuzzy expert system. Weld bead shape is estimated from the welding parameters using fuzzy expert system. The result of comparison between measured values of weld bead by welding experiments and the estimates values by fuzzy expert system shows a good consistancy.

• Journal of Information Technology Services
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• v.11 no.3
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• pp.1-16
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• 2012

This paper is aimed to present a fuzzy decision-making approach to deal with disaster recovery priority decision problem in information system. We derive an evaluation approach based on TOPSIS(Technique for Order Performance by Similarity to Ideal Solution), to help disaster recovery priority decision of total information system for port logistics in a fuzzy environment where the vagueness and subjectivity are handled with linguistic terms parameterized by trapezoidal fuzzy numbers. This study applies the fuzzy multi-criteria decision-making method to determine the importance weight of evaluation criteria and to synthesize the ratings of candidate disaster recovery system. Aggregated the evaluators' attitude toward preference, then TOPSIS is employed to obtain a crisp overall performance value for each alternative to make a final decision. This approach is demonstrated with a real case study involving 4 evaluation criteria(system dependence, RTO, loss, alternative business support), 7 information systems for port logistics assessed by 5 evaluators from Maritime Affairs and Port Office.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.5
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• pp.482-494
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• 1992

In this paper, the neuro-fuzzy inferene system for the effective object recognition is studied. The proposed neuro-fuzzy inference system combines learning capability of neural network with inference process of fuzzy theory, and the system executes the fuzzy inference by neural network automatically. The proposed system consists of the antecedence neural network, the consequent neural network, and the fuzzy operational part, For dissolving the ambiguity of recognition due to input variance in the neuro-fuzzy inference system, the antecedence’s fuzzy proposition of the inference rules are automatically produced by error back propagation learining rule. Therefore, when the fuzzy inference is made, the shape of membership functions os adaptively modified according to the variation. The antecedence neural netwerk constructs a separated MNN(Model Classification Neural Network)and LNN(Line segment Classification Neural Networks)for dissolving the degradation of recognition rate. The antecedence neural network can overcome the limitation of boundary decisoion characteristics of nrural network due to the similarity of extracted features. The increased recognition rate is gained by the consequent neural network which is designed to learn inference rules for the effective system output.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.1
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• pp.69-73
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• 2003

Neuro-Fuzzy System is to combine merits of fuzzy inference system and neural networks. The neuro-fuzzy system applies a information about given input-output data to fuzzy theories and deals these fuzzy values with neural networks, e.g. first, redefines normalized input-output data as membership functions and then executes thses fuzzy information with backpropagation neural networks. This paper describes an innovative application of the Taguchi method for the determination of these parameters to meet the training speed and accuracy requirements. Results drawn from this research show that the Taguchi method provides an effective means to enhance the performance of the neuro-fuzzy system in terms of the speed for learning and the accuracy for recall.